1. Field of the Invention
This invention relates to the assembly of electronic devices, and more particularly concerns the system for diagnosing defects in electronic assemblies.
2. Description of Related Art
Electronic assemblies often require a relatively large number of components to perform a given task. As the number of components used in such assemblies increases, the probability that the assembly contains at least one defect also increases for several reasons. First, though the probability that a particular component has a defect may be relatively low, the probability of having a defect in an assembly with a large number of components may be relatively high. In addition, components which originally had no defects may become defective during assembly due to the nature of the assembly process (e.g., such as by exposure to static electricity). Because of these factors, together with the high degree of reliability which must be achieved in certain types of electronic assemblies (e.g., assemblies used in missile guidance systems), it is often desirable to test each assembly prior to incorporation into the finished product. Such testing can be expensive in terms of cost and production time.
Computer assisted diagnosis of faulty electronic assemblies has been recognized as one way of minimizing these costs. Systems which perform computer assisted diagnosis included fault-tree programs and guided probe hardware and software. While these systems are generally effective under certain circumstances, they often have one or more of the following shortcomings:
development is highly labor intensive PA1 use is highly labor intensive PA1 incapable of learning from prior misdiagnoses. PA1 relatively low success rates PA1 tend to be complex and difficult to understand PA1 often not suitable for the computer integrated manufacturing environment.
Another possible approach involving computer assisted diagnosis is the use of expert systems. Expert systems are characterized as computing systems which are capable of representing and reasoning about some knowledge-rich domain with a view to solving problems and giving advice. Such expert systems have been used in the diagnosis and treatment of blood disorders (i.e., MYCIN), the diagnosis of human disease (i.e., INTERNIST), and to assist geologists in "hard rock" mineral exploration (i.e., PROSPECTOR). Traditional expert systems are generally characterized as being rule-based in that the domain expert establishes a set of rules which causes the system to respond in a certain manner to a given input.
There are however several disadvantages of using conventional expert systems in diagnosing electronic assemblies. First, there may be no true domain expert available to permit knowledge acquisition, and it may be difficult to formulate the expertise into appropriate rules. In addition, the domain expert may not be able to provide sufficient knowledge to the system to achieve a high success rate. Further, the knowledge acquisition process is usually highly labor intensive, and must generally occur each time a new assembly type is to be diagnosed or when a product change occurs. Finally, the interaction with an expert system during diagnosis is generally highly labor intensive.